Structural damage localization through multi-channel empirical mode decomposition

2019 ◽  
Vol 10 (1) ◽  
pp. 102-117 ◽  
Author(s):  
Mehdi Salehi ◽  
Mansour Azami
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Flexural Rigidity ◽  
Structural Vibration ◽  
Damage Localization ◽  
Content Type ◽  
Vibrational Response ◽  
Response Data ◽  
Mode Decomposition ◽  
Complicated Geometries

Purpose The purpose of this paper is to develop a new structural damage detection technique based on multi-channel empirical mode decomposition (MEMD) of vibrational response data. Design/methodology/approach Empirical mode decomposition (EMD) is an empirical data-based signal decomposition method which has been applied in many engineering problems. Utilizing classical EMD to reveal the damage-indicating features of structural vibration response encounters some difficulties due to the inconsistency of modes obtained from different data channels. To overcome this problem, MEMD has been employed. To this end, MEMD algorithm has been adopted to impulse response vector of measured DOFs. The proposed method has been carried out concerning both numerical and experimental beam models. Damage has been modeled by reducing the flexural rigidity in some predefined beam sections. The effects of various factors such as measurement grid density, damage severity and damage position are investigated. Findings The results of both numerical and experimental case studies have been promising. The method could determine the damage location in all cases. The efficiency of method gets better when damage is located far from inflation points of the corresponding mode. In such cases, utilizing higher modes can make up the efficiency. Research limitations/implications Since the present research is the first investigation of MEMD in damage localization, just one-dimensional structures have been studied. Extending the method to more complicated geometries needs further attempt. Originality/value Although a number of relevant studies have been carried out based on EMD, up to the author’s best knowledge, this is the first attempt to structural damage localization using MEMD.

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance

2019 ◽  
Vol 26 (11-12) ◽  
pp. 1012-1027 ◽  
Author(s):  
Hassan Sarmadi ◽  
Alireza Entezami ◽  
Mohammadhassan Daneshvar Khorram
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Ensemble Empirical Mode Decomposition ◽  
Ambient Vibration ◽  
Damage Localization ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Stationary Vibration

Damage localization of damaged structures is an important issue in structural health monitoring. In data-based methods based on statistical pattern recognition, it is necessary to extract meaningful features from measured vibration signals and utilize a reliable statistical technique for locating damage. One of the challenging issues is to extract reliable features from non-stationary vibration signals caused by ambient excitation sources. This article proposes a new energy-based method by using ensemble empirical mode decomposition and Mahalanobis-squared distance to obtain energy-based multivariate features and locate structural damage under ambient vibration and non-stationary signals. The main components of the proposed method include extracting intrinsic mode functions of vibration signals by ensemble empirical mode decomposition, choosing adequate and optimal intrinsic mode functions, partitioning the selected intrinsic mode functions at each sensor into segments with the same dimensions, calculating the intrinsic mode function energy at each segment, preparing energy-based multivariate features at each sensor, computing Mahalanobis-squared distance values, and obtaining a vector of average Mahalanobis-squared distance quantities of all sensors. The major contributions of the proposed method consist of proposing an innovative non-parametric strategy for feature extraction, presenting generalized Pearson correlation function for the selection of optimal intrinsic mode functions, using a simple and effective segmentation algorithm, and applying energy-based features to the process of damage localization. The main advantage of the proposed method is its great applicability to locating single and multiple damage cases. The measured vibration responses of the well-known IASC-ASCE structure are applied to verify the effectiveness and reliability of the proposed energy-based method along with several comparative studies. Results will demonstrate that this approach is highly capable of locating damage under stationary and non-stationary vibration signals attributable to ambient excitations.

Combining Pole Placement and Online Empirical Mode Decomposition Methods to Adaptive Active Control of Structural Vibration

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
S. H. Momeni Massouleh ◽  
S. A. Hosseini Kordkheili ◽  
H. Mohammad Navazi ◽  
H. Bahai
Keyword(s):  
Empirical Mode Decomposition ◽  
Active Control ◽  
Pole Placement ◽  
Structural Vibration ◽  
Control Technique ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Optimal Gains ◽  
Excitation Frequencies ◽  
Emd Method

Using a combination of the pole placement and online empirical mode decomposition (EMD) methods, a new algorithm is proposed for adaptive active control of structural vibration. The EMD method is a time-frequency domain analysis method that can be used for nonstationary and nonlinear problems. Combining the EMD method and Hilbert transform, which is called Hilbert–Huang transform, achieves a method that can be implemented to extract instantaneous properties of signals such as structural response dominant instantaneous frequencies. In the proposed algorithm, these estimated instantaneous properties are utilized to improve the pole-placement method as an adaptive active control technique. The required active control gains are obtained using a genetic algorithm scheme, and optimal gains are calculated corresponding to preselected excitation frequencies. An algorithm is also introduced to choose excitation frequencies based on online EMD method resolution. In order to investigate the efficiency of the proposed method, some case studies that include a discrete model, continuous samples of beam and plate structures, and experimental cantilevered beam are carried out, and the results of the proposed method are compared with the preset (nonadaptive) optimal gains conditions.

Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanmei Huang ◽  
Changrui Deng ◽  
Xiaoyuan Zhang ◽  
Yukun Bao
Keyword(s):  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Stock Price ◽  
Price Index ◽  
Support Vector ◽  
Modeling Framework ◽  
Content Type ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Stock Price Index

Purpose Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables. Design/methodology/approach Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model. Findings The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets. Originality/value This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

A pseudo-modal structural damage index based on orthogonal empirical mode decomposition

2019 ◽  
Vol 233 (23-24) ◽  
pp. 7545-7564 ◽  
Author(s):  
Egidio Lofrano ◽  
Francesco Romeo ◽  
Achille Paolone
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Degrees Of Freedom ◽  
Damage Identification ◽  
Damage Index ◽  
Experimental Tests ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Identification Technique ◽  
Output Only

A structural damage identification technique hinged on the combination of orthogonal empirical mode decomposition and modal analysis is proposed. The output-only technique is based on the comparison between pre- and post-damage free structural vibrations signals. The latter are either kinematic (displacements, velocities or accelerations) or deformation measures (strains or curvatures). The response data are decomposed by means of the orthogonal empirical mode decomposition to derive a finite set of orthogonal intrinsic mode functions; the latter are used as a multi-frequency and data-driven basis to build pseudo-modal shapes. A new damage index, the so-called pseudo-mode index, is introduced to compare the response obtained for the two states of the structural system and detect potential damages. The performance of the devised index in detecting a localised damage is shown through numerical and experimental tests on two structural models, namely a 4-degrees-of-freedom system and a two-hinged parabolic arch.

Spin flight mode identification with OEEMD algorithm

2019 ◽  
Vol 91 (4) ◽  
pp. 582-600
Author(s):  
S. Abolfazl Mokhtari ◽  
Mehdi Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Complex Signals ◽  
Content Type ◽  
Mode Decomposition ◽  
Flight Mode ◽  
Highly Nonlinear ◽  
Mixing Problem ◽  
Mode Mixing

Purpose The paper aims to present an innovative method for identification of flight modes in the spin maneuver, which is highly nonlinear and coupled dynamic. Design/methodology/approach To fix the mode mixing problem which is mostly happen in the EMD algorithm, the authors focused on the proposal of an optimized ensemble empirical mode decomposition (OEEMD) algorithm for processing of the flight complex signals that originate from FDR. There are two improvements with the OEEMD respect to the EEMD. First, this algorithm is able to make a precise reconstruction of the original signal. The second improvement is that the OEEMD performs the task of signal decomposition with fewer iterations and so with less complexity order rather than the competitor approaches. Findings By applying the OEEMD algorithm to the spin flight parameter signals, flight modes extracted, then with using systematic technique, flight modes characteristics are obtained. The results indicate that there are some non-standard modes in the nonlinear region due to couplings between the longitudinal and lateral motions. Practical implications Application of the proposed method to the spin flight test data may result accurate identification of nonlinear dynamics with high coupling in this regime. Originality/value First, to fix the mode mixing problem in EMD, an optimized ensemble empirical mode decomposition algorithm is introduced, which disturbed the original signal with a sort of white Gaussian noise, and by using white noise statistical characteristics the OEEMD fix the mode mixing problem with high precision and fewer calculations. Second, by applying the OEEMD to the flight output signals and with using the systematic method, flight mode characteristics which is very important in the simulation and controller designing are obtained.

Sign in / Sign up

Export Citation Format

Share Document